1 citations · 1 across the 7 of their papers we have counts for
8 papers
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Beyond N-gram: Data-Aware X-GRAM Extraction for Efficient Embedding Parameter Scaling
Yilong Chen, Yanxi Xie, Zitian Gao +10
Large token-indexed lookup tables provide a compute-decoupled scaling path, but their practical gains are often limited by poor parameter efficiency and rapid memory growth. We att…
From Atoms to Trees: Building a Structured Feature Forest with Hierarchical Sparse Autoencoders
Yifan Luo, Yang Zhan, Jiedong Jiang +4
Sparse autoencoders (SAEs) have proven effective for extracting monosemantic features from large language models (LLMs), yet these features are typically identified in isolation. H…
InverseScope: Scalable Activation Inversion for Interpreting Large Language Models
Yifan Luo, Zhennan Zhou, Bin Dong
Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing feature interpretability methods often rely…
Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting
Yifan Luo, Zhennan Zhou, Meitan Wang +1
In this paper, we investigate the safety mechanisms of instruction fine-tuned large language models (LLMs). We discover that re-weighting MLP neurons can significantly compromise a…
Prompt Engineering Through the Lens of Optimal Control
Yifan Luo, Yiming Tang, Chengfeng Shen +2
Prompt Engineering (PE) has emerged as a critical technique for guiding Large Language Models (LLMs) in solving intricate tasks. Its importance is highlighted by its potential to s…
Fictitious Play for Mean Field Games with Optimal Stopping: Convergence and Computation
Chengfeng Shen, Yifan Luo, Zhennan Zhou +1
This paper studies mean field games with optimal stopping time (OSMFGs) where agents make optimal exit decisions. Such a model features a coupled obstacle problem and Fokker-Planck…